When delivering software that accomplishes mission critical, high availability, high demand services at a large scale such as in the telecommunications industry, it is a must to ensure the products' best quality before the products are delivered to the customers. It is therefore essential to perform a large number of tests, including non-functional (load) testing on environments that are as realistic as possible by using inflated databases that will be as similar to real productions as possible, from both volume and data consistency aspects.
Additionally, the databases need to be configured in a way that is consistent with any business flows of the global system. The databases must also be provisioned with data, as if the data in the databases is a result from exact operations of the system.
The configuration and provisioning process of the database is called inflation. Inflating databases induces two major challenges, specifically, the need to have the most realistic data and to be able to inflate the databases in the minimum time possible.
Most of the current database inflation techniques deal with synthetic data or, in other cases, use automation that records the application user interface and replays the recording. Both techniques have drawbacks, as it is time and resource consuming to implement and replay actions, especially when products are at early development stages and are not stable enough.
There is thus a need for addressing these and/or other issues associated with the prior art.